Modeling of an SCN5A founder mutation in iPSC-derived cardiomyocytes

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Research Foundation - Flanders (FWO) Introduction SCN5A encodes the α-subunit of voltage-gated cardiac sodium channel Nav1.5. Mutations in SCN5A are identified in about 20% of patients with Brugada syndrome (BrS), an inherited cardiac arrhythmia. We have identified an SCN5A founder mutation (c.4813+3_4813+6dupGGGT), leading to a loss-of-function of Nav1.5 in 25 different families. Mutation carriers show variable expression of the phenotype: from asymptomatic to syncopes and sudden cardiac death. We used induced pluripotent stem cell derived cardiomyocytes (iPSC-CM) to investigate the underlying pathophysiology. Material &amp; Methods Dermal fibroblasts of six patients with different disease severity, and two unrelated healthy control individuals were reprogrammed using a commercially available reprogramming kit. iPSC-CMs were differentiated following a published protocol. We performed several differentiation rounds and investigated expression of cardiac markers using qPCR and immunocytochemistry and electrophysiological properties using patch-clamping. Results All iPSC-CMs expressed the tested markers. We observed reduction in sodium current density in patient iPSC-CMs compared to the control cells. However, our data display variability in AP characteristics between the differentiation batches, as well as between clones generated from one donor. Conclusions We established iPSC-CM models for a unique Belgian SCN5A founder mutation. Despite the observed variability, we could detect expected differences in electrophysiological properties of patient cells compared to controls. </jats:sec

Improved selection of zebrafish CRISPR editing by early next-generation sequencing based genotyping.

Despite numerous prior attempts to improve knock-in (KI) efficiency, the introduction of precise base pair substitutions by the CRISPR-Cas9 technique in zebrafish remains challenging. In our efforts to generate KI zebrafish models of human CACNA1C mutations, we have tested the effect of several CRISPR determinants on KI efficiency across two sites in a single gene and developed a novel method for early selection to ameliorate KI efficiency. We identified optimal KI conditions for Cas9 protein and non-target asymmetric PAM-distal single stranded deoxynucleotide repair templates at both cacna1c sites. An effect of distance to the cut site on the KI efficiency was only observed for a single repair template conformation at one of the two sites. By combining minimally invasive early genotyping with the zebrafish embryo genotyper (ZEG) device and next-generation sequencing, we were able to obtain an almost 17-fold increase in somatic editing efficiency. The added benefit of the early selection procedure was particularly evident for alleles with lower somatic editing efficiencies. We further explored the potential of the ZEG selection procedure for the improvement of germline transmission by demonstrating germline transmission events in three groups of pre-selected embryos

Artificial intelligence-enhanced electrocardiography for the identification of a sex-related cardiovascular risk continuum: a retrospective cohort study

Abstract: Background Females are typically underserved in cardiovascular medicine. The use of sex as a dichotomous variable for risk stratification fails to capture the heterogeneity of risk within each sex. We aimed to develop an artificial intelligence-enhanced electrocardiography (AI-ECG) model to investigate sex-specific cardiovascular risk. Methods In this retrospective cohort study, we trained a convolutional neural network to classify sex using the 12-lead electrocardiogram (ECG). The Beth Israel Deaconess Medical Center (BIDMC) secondary care dataset, comprising data from individuals who had clinically indicated ECGs performed in a hospital setting in Boston, MA, USA collected between May, 2000, and March, 2023, was the derivation cohort (1 163 401 ECGs). 50% of this dataset was used for model training, 10% for validation, and 40% for testing. External validation was performed using the UK Biobank cohort, comprising data from volunteers aged 40-69 years at the time of enrolment in 2006-10 (42 386 ECGs). We examined the difference between AI-ECG-predicted sex (continuous) and biological sex (dichotomous), termed sex discordance score. Findings AI-ECG accurately identified sex (area under the receiver operating characteristic 0943 [95% CI 0942-0943] for BIDMC and 0971 [0969-0972] for the UK Biobank). In BIDMC outpatients with normal ECGs, an increased sex discordance score was associated with covariate-adjusted increased risk of cardiovascular death in females (hazard ratio [HR] 178 [95% CI 118-270], p=0006) but not males (100 [063-158], p=0996). In the UK Biobank cohort, the same pattern was seen (HR 133 [95% CI 106-168] for females, p=0015; 098 [080-120] for males, p=0854). Females with a higher sex discordance score were more likely to have future heart failure or myocardial infarction in the BIDMC cohort and had more male cardiac (increased left ventricular mass and chamber volumes) and non- cardiac phenotypes (increased muscle mass and reduced body fat percentage) in both cohorts. Interpretation Sex discordance score is a novel AI-ECG biomarker capable of identifying females with disproportionately elevated cardiovascular risk. AI-ECG has the potential to identify female patients who could benefit from enhanced risk factor modification or surveillance. . Copyright (c) 2025 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license

Artificial intelligence-enhanced electrocardiography for the identification of a sex-related cardiovascular risk continuum : a retrospective cohort study

Background Females are typically underserved in cardiovascular medicine. The use of sex as a dichotomous variable for risk stratification fails to capture the heterogeneity of risk within each sex. We aimed to develop an artificial intelligence-enhanced electrocardiography (AI-ECG) model to investigate sex-specific cardiovascular risk. Methods In this retrospective cohort study, we trained a convolutional neural network to classify sex using the 12-lead electrocardiogram (ECG). The Beth Israel Deaconess Medical Center (BIDMC) secondary care dataset, comprising data from individuals who had clinically indicated ECGs performed in a hospital setting in Boston, MA, USA collected between May, 2000, and March, 2023, was the derivation cohort (1 163 401 ECGs). 50% of this dataset was used for model training, 10% for validation, and 40% for testing. External validation was performed using the UK Biobank cohort, comprising data from volunteers aged 40–69 years at the time of enrolment in 2006–10 (42 386 ECGs). We examined the difference between AI-ECG-predicted sex (continuous) and biological sex (dichotomous), termed sex discordance score. Findings AI-ECG accurately identified sex (area under the receiver operating characteristic 0·943 [95% CI 0·942–0·943] for BIDMC and 0·971 [0·969–0·972] for the UK Biobank). In BIDMC outpatients with normal ECGs, an increased sex discordance score was associated with covariate-adjusted increased risk of cardiovascular death in females (hazard ratio [HR] 1·78 [95% CI 1·18–2·70], p=0·006) but not males (1·00 [0·63–1·58], p=0·996). In the UK Biobank cohort, the same pattern was seen (HR 1·33 [95% CI 1·06–1·68] for females, p=0·015; 0·98 [0·80–1·20] for males, p=0·854). Females with a higher sex discordance score were more likely to have future heart failure or myocardial infarction in the BIDMC cohort and had more male cardiac (increased left ventricular mass and chamber volumes) and non-cardiac phenotypes (increased muscle mass and reduced body fat percentage) in both cohorts. Interpretation Sex discordance score is a novel AI-ECG biomarker capable of identifying females with disproportionately elevated cardiovascular risk. AI-ECG has the potential to identify female patients who could benefit from enhanced risk factor modification or surveillance

Prognostic significance and associations of neural network-derived electrocardiographic features

BACKGROUND: Subtle, prognostically important ECG features may not be apparent to physicians. In the course of supervised machine learning, thousands of ECG features are identified. These are not limited to conventional ECG parameters and morphology. We aimed to investigate whether neural network–derived ECG features could be used to predict future cardiovascular disease and mortality and have phenotypic and genotypic associations. METHODS: We extracted 5120 neural network–derived ECG features from an artificial intelligence–enabled ECG model trained for 6 simple diagnoses and applied unsupervised machine learning to identify 3 phenogroups. Using the identified phenogroups, we externally validated our findings in 5 diverse cohorts from the United States, Brazil, and the United Kingdom. Data were collected between 2000 and 2023. RESULTS: In total, 1 808 584 patients were included in this study. In the derivation cohort, the 3 phenogroups had significantly different mortality profiles. After adjusting for known covariates, phenogroup B had a 20% increase in long-term mortality compared with phenogroup A (hazard ratio, 1.20 [95% CI, 1.17–1.23]; P<0.0001; phenogroup A mortality, 2.2%; phenogroup B mortality, 6.1%). In univariate analyses, we found phenogroup B had a significantly greater risk of mortality in all cohorts (log-rank P<0.01 in all 5 cohorts). Phenome-wide association study showed phenogroup B had a higher rate of future atrial fibrillation (odds ratio, 2.89; P<0.00001), ventricular tachycardia (odds ratio, 2.00; P<0.00001), ischemic heart disease (odds ratio, 1.44; P<0.00001), and cardiomyopathy (odds ratio, 2.04; P<0.00001). A single-trait genome-wide association study yielded 4 loci. SCN10A, SCN5A, and CAV1 have roles in cardiac conduction and arrhythmia. ARHGAP24 does not have a clear cardiac role and may be a novel target. CONCLUSIONS: Neural network–derived ECG features can be used to predict all-cause mortality and future cardiovascular diseases. We have identified biologically plausible and novel phenotypic and genotypic associations that describe mechanisms for the increased risk identified